How to Use the Worktile MCP in LlamaIndex

Q: How does LlamaIndex handle Worktile project queries?

LlamaIndex indexes the results of your calls, such as listprojects. You don't just get a list; you create a searchable document from that list. Your AI agent can then query 'Which projects were created last month?' and find the answer in the index.

Q: Can LlamaIndex use Worktile to build knowledge over time?

Yes. Every time you execute a tool, like sendmessage or updatetask, the resulting data can be incorporated into your vector store. This means your knowledge base grows with every interaction.

Q: What type of Worktile data does this MCP Server touch in LlamaIndex?

It handles structural and communication data. Tools like getteaminfo, listchannels, and task metadata (from listtasks) are indexed, allowing you to query relationships and content.

Q: Does LlamaIndex support filtering specific Worktile tool usage?

Yes. The client setup allows you to use the allowedtools filter. This means you can restrict your knowledge index only to data derived from specific tools, like making sure you only query information gathered from listprojects.

Index Worktile data results into a searchable knowledge base with LlamaIndex.

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Connect Worktile MCP to LlamaIndex

Create your Vinkius account to connect Worktile to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Worktile with LlamaIndex

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Building Searchable Project Histories

Need to recall old project details? Run `get_project` and index the output. Later, you can ask questions about that data—like 'What was the budget for Q3?'—and get answers grounded in actual API results stored in your vector store.

Querying Member Roles Across Teams

Use `list_members` to pull organization details. By indexing this output, you create a knowledge base of who reports to whom. You can then query that index later to understand team dependencies without making another live API call.

Retrieving Task Status and History

When an agent calls `list_tasks`, the output detailing task names, states, and owners gets indexed. This lets you build a comprehensive RAG application that answers questions like 'Which tasks are currently stuck in review?' using historical data.

Setup guide

Set up Worktile MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Worktile MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Worktile tools.",
)
response = await agent.run("List recent Worktile data")

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Common questions about Worktile MCP in LlamaIndex

LlamaIndex indexes the results of your calls, such as `list_projects`. You don't just get a list; you create a searchable document from that list. Your AI agent can then query 'Which projects were created last month?' and find the answer in the index.

Yes. Every time you execute a tool, like `send_message` or `update_task`, the resulting data can be incorporated into your vector store. This means your knowledge base grows with every interaction.

The key benefit is that you stop relying on live API calls for everything. You capture the current state—like all team members from `list_members`—and make it permanent, giving your agent verifiable data.

It handles structural and communication data. Tools like `get_team_info`, `list_channels`, and task metadata (from `list_tasks`) are indexed, allowing you to query relationships and content.

Yes. The client setup allows you to use the allowed_tools filter. This means you can restrict your knowledge index only to data derived from specific tools, like making sure you only query information gathered from `list_projects`.

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